Causal relationships between plasma lipids and sepsis: A Mendelian randomization study

Although observational studies have indicated that plasma lipids are associated with an increased risk of sepsis, due to confounders and reverse causality, the causal relationship remains unclear. This study was designed to assess the causal effects of plasma lipid levels on sepsis. We used a 2-sample Mendelian randomization (MR) method to evaluate the causal effect of plasma lipids on sepsis. MR analysis employs methods such as inverse variance weighted, MR-Egger regression, weighted median regression (WME), simple mode and weighted mode. The inverse variance weighted (IVW) method was predominantly utilized to assess causality. Heterogeneity was affirmed by Cochran Q test, while pleiotropy was corroborated by MR-Egger regression analysis. The robustness and reliability of the results were demonstrated through “leave-one-out” sensitivity analysis. Instrumental variables included 226 single-nucleotide polymorphisms (SNPs), comprising of 7 for triglyceride (TG), 169 for high-density lipoprotein cholesterol (HDL-C), and 50 for low-density lipoprotein cholesterol (LDL-C). The risk of sepsis appeared to increase with rising LDL-C levels, as indicated by the inverse variance weighted analysis (OR 1.11, 95% CI from0.99 to1.24, P = 0.068). However, no causality existed between LDL-C, HDL-C, TG and sepsis. Two-sample MR analysis indicated that increased LDL-C level is a risk factor for sepsis, while TG and HDL-C levels have protective effects against sepsis. However, no significant causal relationship was found between TG, HDL-C, and LDL-C levels and sepsis.


Introduction
Sepsis is a severe condition that can lead to life-threatening organ dysfunction.It is fundamentally characterized by inflammation and immune dysregulation provoked by infection, [1] with prominent features such as long-term disease onset, high mortality rate in late stages, and chronic severity. [2]n 2017, approximately 48.9 million people worldwide were affected by sepsis.Sepsis poses a significant challenge to global health. [3]Despite significant advancements in medical technology, The incidence and mortality rates attributed to sepsis continue to increase, and survivors of sepsis commonly endure a diminished quality of life.Notably, the mortality risk remains elevated, [4] presenting substantial socio-economic costs. [5,6]veral patients may endure complications, encompassing physiological, psychological, and cognitive functional impairments. [7]The treatment cost for sepsis is considered the highest among all diseases in Intensive Care Units. [8]Currently, there is no specific treatment available for sepsis.Consequently, it is crucial to identify risk factors that influence sepsis onset and prognosis.
Plasma lipids include triglyceride (TG), total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), and high-density lipoprotein cholesterol (HDL-C).Triglyceride represents the largest energy storage in the human body and accumulate in the cytoplasm of fat cells. [9]In the early stages of sepsis, the level of fat breakdown in adipose tissue is increased, [10,11] leading to elevated levels of triglycerides, with septic patients showing plasma triglyceride concentrations approximately 3 times higher than that in healthy individuals. [12][15] Although cholesterol is a risk factor for cardiovascular and cerebrovascular events, it offers a protective effect in sepsis. [16]LDL-C can promote the clearance of bacterial toxins, [17] whereas HDL-C can bind to bacterial toxins, preventing the release of inflammatory factors. [16,18]There is a decrease in the levels of HDL-C that can predict the risk of infection and worsening outcomes in sepsis. [19]22] Cardiovascular risks may be provoked by abnormal blood lipid levels, but the causal effect of lipids on sepsis is not yet clear.Therefore, studies utilizing Mendelian randomization (MR) are in progress to investigate the potential causal relationship between plasma lipid levels and sepsis.
MR, originally introduced by Katan (1986), employs genetic variations as instrumental variables to evaluate the causal relationship between exposure and outcomes, thereby mitigating confounding factors and reverse causality. [23]This methodology parallels the conventional randomized controlled trials but is designed to mitigate the inherent confounding elements within randomized controlled trials. [24]In this study, we used a 2-sample MR method to analyze the causal relationship between plasma lipids and sepsis.

Study design
MR, grounded in the principle that varying genotypes dictate distinct intermediate phenotypes representing individual exposures, posits that the correlation between genotype and disease mirrors the impact of the exposure on the disease. [25]As shown in Figure 1, Single nucleotide polymorphisms (SNPs) functioned as instrumental variables (IVs) to infer the association between exposure and outcome.An MR analysis must satisfy 3 fundamental assumptions [26] : relevance Assumption: SNPs must have a strong association with exposure, exclusion Restriction Assumption: The SNPs should not be associated with the outcome, and independence assumption: SNPs were not associated with any confounding factors.The flowchart of the study is shown in Figure 2.

Selection of instrumental variables
We selected instrumental variables (IVs) in the following manner: we chose significant SNPs (P < 5.0 × 10 −8 ) as instrumental variables.The F-statistics were computed using the subsequent equation to evaluate the potency of each instrumental variable (IV).To mitigate the bias induced by ineffective instrumental variables (IVs), those with an F-statistic <10 were omitted. [27]he F statistics was formulated as , "R2" signifies the proportion of variation accounted for by the SNPs present in the exposure, whereas "n" illustrates the sample size and "K" indicates the total number of Instrumental Variables (IVs).and we set the imbalance ratio r 2 < 0.001, and set kb equal to 10,000 to minimize the interference from linkage disequilibrium. [28]We excluded SNPs related to confounding factors, including body mass index, chronic liver disease, infection, andage, etc, using the Phenoscanner database (http://www.phenoscanner.medschl.cam.ac.uk/). [29]

Statistical analysis for mendelian randomization
MR analysis employs methods such as inverse variance weighted, MR-Egger regression, [30] weighted median regression, [31] simple mode and weighted mode.The inverse variance weighted (IVW) method was predominantly utilized to assess causality.The Inverse Variance Weighting (IVW) method, adopting multiplication effects, is utilized to ascertain the causal relationship between exposure and outcomes.Predicated on weighted least squares regression, the Inverse Variance Weighting (IVW) method employs genetic variation as an instrumental variable to gauge the resultant influence of exposure factors on given outcomes.Contrasting with many regression models, the IVW regression does not incorporate an intercept term.Consequently, the final outcome manifests as the weighted average of the effect values attributed to all instrumental variables (IVs).bycontrast, MR-Egger regression considers the existence of an intercept term.The Weighted Median Estimate represents the median of the distribution function, which is determined after arranging the SNP effect values based on their respective weights, allowing for the correct estimation of cause-effect relationships even when up to 50% of the instrumental variables are invalid . [31]IVW was the primary analysis method in this study, with MR-Egger and the weighted median method used to supplement the IVW estimation.Heterogeneity was affirmed by Cochran Q test, [32] while pleiotropy was corroborated by MR-Egger regression analysis.The robustness and reliability of the results were demonstrated through "leave-one-out" sensitivity analysis.There was no evidence of directional pleiotropy, as indicated by an Egger-intercept from the linear regression approximating zero, and undetectable horizontal pleiotropy of the instrumental variables (IVs), as supported by a P>.05 in the MR-PRESSO global test.Thereby, we could deem the exclusivity assumption as valid.All the above methods were implemented using the TwoSampleMR package in R 4.2.2.Statistically significant difference was considered at P < .05.

Instrumental variables
In the study, we identified 226 independent (r 2 < 0.001) and significantly correlated SNPs (P < 5 × 10 −8 ) to serve as instrumental variables, excluding the following SNPs: rs10119644, rs1125873, rs133015, rs407133, rs990619.Among them, there are 7 triglycerides (TG), 169 high-density lipoproteins (HDL-C), and 50 low-density lipoproteins (LDL-C).The distribution range of the F statistic for a single SNP was 29.86 to 5569.87,This implies that the causal association is less susceptible to distortions caused by weak instrumental variable bias.Detailed results are provided in Table 2.

Sensitivity analysis
The F-statistic for all instrumental variables (IVs) exceeded 10, suggesting a higher precision and accuracy in our estimation of the causal association between TG, HDL-C, LDL-C and sepsis.As shown in Table 2, no directional pleiotropy and horizontal pleiotropy was found no directional pleiotropy and horizontal pleiotropy was found for the analyze of TG, HDL-C and LDL-C.In the funnel plot, with each point representing the causal association effect when employing a single SNP as an IV, revealed a symmetric distribution.This symmetry suggests that causal associations are less prone to potential bias.As can be seen from the funnel plot, all the included SNPs are symmetrical, indicating that the estimation of causal effects using SNPs as IVs experiences relatively small potential impacts.The results of the pleiotropy analysis are presented in a scatter plot.The results indicated that there was no horizontal pleiotropy (TG, egger_intercept = 0.0062, P = .536;HDL-C, egger_intercept = −0.0038,P = .0793;LDL-C, egger_intercept = −0.0035,P = .3692),as shown in Figures 3,  4, and 5.

Discussion
In this study, a 2-sample MR approach was utilized to investigate the causal relationship between plasma lipids and sepsis, using GWAS data.The results indicated that LDL-C is a risk factor for sepsis, while TG and HDL-C are protective factors against sepsis.
In a prospective cohort study of 171 sepsis patients, total cholesterol, LDL-C and HDL-C levels were significantly decreased in 113 bacterial culture positive sepsis cases (TC, P < .001;LDL-C, P < .001;HDL-C, P = .011).Moreover, cholesterol levels differed among sepsis patients with different types of infections, with gram-negative bacteremia patients having significantly lower   cholesterol levels than Gram-positive bacteremia [33] A retrospective study found that among 4512 sepsis patients, both the low-cholesterol group (cholesterol < 120 mg/dL) and the high-cholesterol group (cholesterol > 200 mg/dL) had a higher risk of death at 28 days than the normal cholesterol group (cholesterol 120-200 mg/dL).This suggests that the cholesterol concentration can be used as a prognostic factor for sepsis or septic shock. [14,34]Phase I clinical studies have reported that stable cholesterol levels in early sepsis can be managed using fish oil-infused Lipid Emulsions.However, whether this type of   exogenous lipid emulsion can alleviate complications such as organ failure and mortality rate.still needs further research. [35]DL-C concentrations are positively associated with the alleviation of sepsis, suggesting a potential therapeutic role for HDL-C.Recombinant HDL treatment can protect animal models of sepsis from organ damage and improve survival rates.[36] HDL regulates the inflammatory pathway in sepsis via the transcription regulatory factor ATF3 and can also protect endothelial cells.[37] Lower HDL-C levels are associated with early onset of sepsis, increased risk of organ failures, and higher sepsis related mortality rates.[38,39] Genetic analysis of HDL-C PRS, CETP PRS, and rs1800777 demonstrated a strong association between clinically measured HDL-C levels and sepsis risk.However, this was not correlated with the risk of sepsis or sepsis-related outcomes, suggesting that such beneficial clinical associations could potentially be attributed to confounding factors.[40] The results from this MR study are consistent with the aforementioned observational studies but were unable to prove a causal relationship between triglycerides, low-density lipoprotein, and high-density lipoprotein with sepsis.
In conclusion, this study found LDL-C to be a risk factor for sepsis, while HDL-C and TG acted as protective factors.There was no causal relationship between TG, LDL-C or HDL-C and sepsis.But plasma lipids may have an impact on the progression of sepsis.[41] It suggested that the mortality rate from sepsis may decrease through enhanced lipid clearance of pathogenic bacteria via Low Density Lipoprotein clearance. [41]A converse correlation is evident between serum inflammatory markers and cholesterol concentration. [39]The biological mechanism triggering hypocholesterolemia in sepsis remains unclear and may be connected to factors such as decreased fat intake, intestinal absorption and synthesis, augmented metabolic products, and toxin clearance in Intensive Care Unit (ICU) patients.Lower cholesterol levels can potentially have detrimental effects on immune cells.During a septic episode, pathogen lipids are encapsulated within lipoproteins while the production of low-density lipoprotein diminishes.This Compared with previous observational studies, the use of MR methods in this research greatly minimized the influence of confounding and reverse causality, resulting in more convincing causal relationships.Of course, this study has certain limitations.First, all GWASs data comes from European or mixed populations.At present, there is a lack of sepsis data information for Asia and Africa in the database, so the research results are not applicable to all ethnic groups, and whether these results are consistent among other ethnicities such as Asians needs further investigation.Second, lipids may have a causal relationship with sepsis caused by specific pathogenic factors, which should be considered for a broader study of disease-related sepsis in the future.Third, due to the design of working variable parameters, some lipid indicators were excluded during the screening process, so not all lipid indicators were included in this study.The results of this study only present a causal relationship between triglycerides, low-density lipoprotein, and high-density lipoprotein and sepsis, while other lipid indicators may have a causal relationship with sepsis.

Figure 2 .
Figure 2. The flowchart of the study.

Figure 3 .
Figure 3.The MR results of LDL-C on sepsis.LDL-C = low-density lipoprotein cholesterol, polymorphisms.

Table 1
Summary of the GWAS included in this 2-sample MR study.

Table 2
The Mendelian randomization analysis results with regard to causal efect of TG,HDL-C and LDL-C on sepsis.